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Histochemical Evaluation associated with Heparan Sulfate 3-O-sulfotransferase Phrase within Computer mouse Brain.
Finally, extensive experiments are given to illustrate the effectiveness of the proposed generative model for pipeline network leak detection.This article studies the issue of adaptive event-triggered output-feedback control for switched p-normal nonlinear systems with the unknown homogeneous growth rate. A homogeneous output-feedback controller is first designed for nominal nonlinear systems based on adding one power integrator technique. Then, a dynamic gain technique is introduced to deal with the difficulty caused by the unknown homogeneous growth rate. With an elaborate design of the adaptive law of the dynamic gain, a novel adaptive event-triggered output-feedback controller is developed to ensure that the closed-loop system is globally asymptotically stable. Meanwhile, a new analysis way is proposed to prove that the Zeno behavior is excluded in the event-triggered control system. Finally, two examples are provided to indicate the effectiveness of the proposed control method.This article considers the quasisynchronization of memristive neural networks (MNNs) with communication delays via event-triggered impulsive control (ETIC). In view of the limited communication and bandwidth, we adopt a novel switching event-triggered mechanism (ETM) that not only decreases the times of controller update and the amount of data sent out but also eliminates the Zeno behavior. By using an appropriate Lyapunov function, several algebraic conditions are given for quasisynchronization of MNNs with communication delays. More important, there is no restriction on the derivation of the Lyapunov function, even if it is an increasing function over a period of time. Then, we further propose a switching ETM depending on communication delays and aperiodic sampling, which is more economical and practical and can directly avoid Zeno behavior. Finally, two simulations are presented to validate the effectiveness of the proposed results.The connectivity is an essential property of the connections between the nodes in networks. The efficient determination algorithm for the connectivity of complex directed networks is an important research direction in graph theory. click here Aiming at the determination problem of the strong connectivity of directed networks, we propose an improved algorithm over the Warshall algorithm, which extends the research object to complex directed networks and has only the half time complexity of that of the latter. In addition, this article also takes the lead in research on the determination algorithm for the unilateral connectivity of complex directed networks, and on this basis, we propose an algorithm to efficiently determine the unilateral connectivity. Finally, the above two algorithms are integrated into a unified and efficient algorithm with the time complexity of O(n³+4.5n²). This algorithm can determine not only the strong connectivity but also the unilateral connectivity of complex directed networks.Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based P-V/Q-ω droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properly, but also consumes as little computing time and space as possible to locate the optima. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.
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